Cheminformatic Approaches to Hit-Prioritization and Target Prediction of Potential Anti-MRSA Natural Products

Abstract

The growing resistance of Methicillin-Resistant Staphylococcus aureus (MRSA) to currently prescribed drugs has resulted in the failure of prevention and treatment of different infections caused by the superbug. Therefore, to keep pace with the resistance, there is a pressing need for novel antimicrobial agents, especially from non-conventional sources. Several natural products (NPs) have displayed varying in vitro activities against the pathogen but few of these natural compounds have been studied for their prospects to be potential antimicrobial drug candidates. This may be due to the high cost, tedious, and time-consuming process of conducting the important preclinical tests on these compounds. Hence, there is a need for cost-effective strategies for mining the available data on these natural compounds. This would help to get the knowledge that may guide rational prioritization of “likely to succeed” natural compounds to be developed into potential antimicrobial drug candidates. Cheminformatic approaches in drug discovery enable chemical data mining, in conjunction with unsupervised and supervised learning from available bioactivity data that may unlock the full potential of NPs in antimicrobial drug discovery. Therefore, taking advantage of the available NPs with their known in vitro activity against MRSA, this study conducted cheminformatic and data mining analysis towards hit profiling, hit-prioritization, hit-optimization, and target prediction of anti-MRSA NPs. Cheminformatic profiling was conducted on the 111 anti-MRSA NPs (AMNPs) retrieved from literature. About 20 current drugs for MRSA (CDs) were used as a reference to identify AMNPs with promising prospects to become drug candidates.